An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing Examinations

As the demand for private vehicles rises, there has been a gradual increase in the number of motor vehicles on the roads, leading to a growing concern about addressing traffic safety. Currently, China’s approach to assessing driver capabilities remains rooted in traditional, non-intelligent, and sta...

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Main Authors: Jianguo Gong, Boao Zhang, Yibing Liu, Jiayi Lu, Yuan Ma, Yaoguang Cao
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/13/24/13066
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author Jianguo Gong
Boao Zhang
Yibing Liu
Jiayi Lu
Yuan Ma
Yaoguang Cao
author_facet Jianguo Gong
Boao Zhang
Yibing Liu
Jiayi Lu
Yuan Ma
Yaoguang Cao
author_sort Jianguo Gong
collection DOAJ
description As the demand for private vehicles rises, there has been a gradual increase in the number of motor vehicles on the roads, leading to a growing concern about addressing traffic safety. Currently, China’s approach to assessing driver capabilities remains rooted in traditional, non-intelligent, and standardized evaluation methods based on examination subjects. The traditional model often falls short in providing constructive feedback on a driver’s real-world vehicle handling abilities, as many of the examination subjects can be practiced in advance to achieve a mere passing result, which, undoubtedly, increases the likelihood of underqualified drivers on the road. To address the issues of the current examination-oriented driver evaluation system in China, we propose a road performance assessment model (RPAM) that assesses drivers comprehensively by evaluating their road environment perception and vehicle operation abilities based on an in-vehicle and out-vehicle perception system. The model leverages patterns of the driver’s head posture, along with real-time information on the vehicle’s behavior and the road conditions, to quantify various performance metrics related to reasonable operation processes. These metrics are then integrated to generate a holistic assessment of the driving capabilities. This paper ultimately conducted tests of the RPAM on one actual examination route in Beijing. Two drivers were randomly selected for the examination. The model successfully computed the overall ability scores for each driver, validating the effectiveness.
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spelling doaj.art-4aebc4fe25bb4ebe8eb2d766b96ca5c72023-12-22T13:50:54ZengMDPI AGApplied Sciences2076-34172023-12-0113241306610.3390/app132413066An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing ExaminationsJianguo Gong0Boao Zhang1Yibing Liu2Jiayi Lu3Yuan Ma4Yaoguang Cao5School of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaResearch Institute for Road Safety of MPS, Beijing 100062, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaAs the demand for private vehicles rises, there has been a gradual increase in the number of motor vehicles on the roads, leading to a growing concern about addressing traffic safety. Currently, China’s approach to assessing driver capabilities remains rooted in traditional, non-intelligent, and standardized evaluation methods based on examination subjects. The traditional model often falls short in providing constructive feedback on a driver’s real-world vehicle handling abilities, as many of the examination subjects can be practiced in advance to achieve a mere passing result, which, undoubtedly, increases the likelihood of underqualified drivers on the road. To address the issues of the current examination-oriented driver evaluation system in China, we propose a road performance assessment model (RPAM) that assesses drivers comprehensively by evaluating their road environment perception and vehicle operation abilities based on an in-vehicle and out-vehicle perception system. The model leverages patterns of the driver’s head posture, along with real-time information on the vehicle’s behavior and the road conditions, to quantify various performance metrics related to reasonable operation processes. These metrics are then integrated to generate a holistic assessment of the driving capabilities. This paper ultimately conducted tests of the RPAM on one actual examination route in Beijing. Two drivers were randomly selected for the examination. The model successfully computed the overall ability scores for each driver, validating the effectiveness.https://www.mdpi.com/2076-3417/13/24/13066driver abilityintelligent systemassessment modelsystem architecture
spellingShingle Jianguo Gong
Boao Zhang
Yibing Liu
Jiayi Lu
Yuan Ma
Yaoguang Cao
An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing Examinations
Applied Sciences
driver ability
intelligent system
assessment model
system architecture
title An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing Examinations
title_full An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing Examinations
title_fullStr An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing Examinations
title_full_unstemmed An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing Examinations
title_short An Intelligent Chinese Driver Road Performance Assessment Model (RPAM) for Future Licensing Examinations
title_sort intelligent chinese driver road performance assessment model rpam for future licensing examinations
topic driver ability
intelligent system
assessment model
system architecture
url https://www.mdpi.com/2076-3417/13/24/13066
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